PREPRINT

# A Neural Network Subgrid Model of the Early Stages of Planet Formation

Thomas Pfeil, Miles Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr

Submitted on 8 November 2022

## Abstract

Planet formation is a multi-scale process in which the coagulation of $\mu \mathrm{m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1.5×{10}^{8}\phantom{\rule{0.167em}{0ex}}\mathrm{km}$). Studies are therefore dependent on subgrid models to emulate the micro physics of dust coagulation on top of a large scale hydrodynamic simulation. Numerical simulations which include the relevant physical effects are complex and computationally expensive. Here, we present a fast and accurate learned effective model for dust coagulation, trained on data from high resolution numerical coagulation simulations. Our model captures details of the dust coagulation process that were so far not tractable with other dust coagulation prescriptions with similar computational efficiency.

## Preprint

Comment: 6 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022

Subjects: Astrophysics - Earth and Planetary Astrophysics; Computer Science - Machine Learning